code: def test_directory_iterator_class_mode_input(all_test_images, tmpdir):
      tmpdir.join("class-1").mkdir()
  
      # save the images in the paths
      count = 0
      for test_images in all_test_images:
          for im in test_images:
              filename = str(
                  tmpdir / "class-1" / "image-{}.png".format(count)
              )
              im.save(filename)
              count += 1
  
      # create iterator
      generator = image_data_generator.ImageDataGenerator()
      dir_iterator = generator.flow_from_directory(
          str(tmpdir), class_mode="input"
      )
      batch = next(dir_iterator)
  
      # check if input and output have the same shape
      assert batch[0].shape == batch[1].shape
      # check if the input and output images are not the same numpy array
      input_img = batch[0][0]
      output_img = batch[1][0]
      output_img[0][0][0] += 1
      assert input_img[0][0][0] != output_img[0][0][0]
  
file_path: keras-preprocessing/tests/image/directory_iterator_test.py
imports: import os
  import shutil
  import tempfile
  import numpy
  import pytest
  from PIL import Image
  from keras_preprocessing.image import image_data_generator
label_desc: Use image node_id 3 for calling a global function with example usage: test_directory_iterator_class_mode_input(all_test_images, tmpdir) without return types